Centered Kernel Alignment (CKA) [1]}, [2]}, [3]} is a method for comparing representations of neural networks, and identifying correspondences between layers, not only in the same network but also on different neural network architectures. CKA is a normalized form of Hilbert-Schmidt Independence Criterion (HSIC) [4]}. Formally, CKA is defined as:
\(\text{CKA}\left(K, L\right) = \cfrac{\text{HSIC}\left(K, L\right)}{\sqrt{\text{HSIC}\left(K, K\right)\cdot \text{HSIC}\left(L, L\right)}}\) 
